Where do you get your data on the Diamond Princess? As far as I know, there are no updates on symptoms. Perhaps you get it from this, which is not data, but an inference?
Is there reason to believe the raw numbers are more accurate estimate of the rate than the model prediction? Also, what are the type-1 and type-2 errors of the tests used on the Diamond Princess? I heard some early reports that both of these might be significant, but then never heard anything about them again.
I checked that link above and followed their references to find other datasets, but two of them are in Japanese, one only deals with self-selected patients who showed symptoms, and the last two have small sample size (12 patients, two papers cover the same event).
Update: I have found https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.3.2000045, which benchmarks the real-time reverse transcription polymerase chain reaction (RT-PCT) tests. They state zero false positives in a trial with 297 non-COVID-19 samples, although they do retest 4 samples that showed “weak initial reactivity”. Since the non real-time version of RT-PCT is supposed to be even more reliable, this means false positives are presumably not a big deal (even at a pessimistic 4/297 false positive this still means only 41 false positives out of 3063 tests done on the Diamond Princess).
I don’t have a good model to give me any predictions on what reasonable numbers of asymptomatic cases would be, or how truncation influences these numbers. Could you explain why the inference is idiotic, and perhaps give a more reasonable one?
I am trying to find the Japanese government webpage with frequent updates as to the state of patients that were evacuated, still updating the ones that are still in the hospital (! Morbidities...)
Where do you get your data on the Diamond Princess? As far as I know, there are no updates on symptoms. Perhaps you get it from this, which is not data, but an inference?
Is there reason to believe the raw numbers are more accurate estimate of the rate than the model prediction? Also, what are the type-1 and type-2 errors of the tests used on the Diamond Princess? I heard some early reports that both of these might be significant, but then never heard anything about them again.
I checked that link above and followed their references to find other datasets, but two of them are in Japanese, one only deals with self-selected patients who showed symptoms, and the last two have small sample size (12 patients, two papers cover the same event).
Update: I have found https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.3.2000045, which benchmarks the real-time reverse transcription polymerase chain reaction (RT-PCT) tests. They state zero false positives in a trial with 297 non-COVID-19 samples, although they do retest 4 samples that showed “weak initial reactivity”. Since the non real-time version of RT-PCT is supposed to be even more reliable, this means false positives are presumably not a big deal (even at a pessimistic 4/297 false positive this still means only 41 false positives out of 3063 tests done on the Diamond Princess).
First of all, it is very important to distinguish data from inferences.
Second, the inference is idiotic. It’s probably a calculation error, but it’s just not worth reading to determine what went wrong.
I don’t have a good model to give me any predictions on what reasonable numbers of asymptomatic cases would be, or how truncation influences these numbers. Could you explain why the inference is idiotic, and perhaps give a more reasonable one?
Here are some quotes from the paper. What is the simplest model you can make from them? Forget the word “model”; what conclusions can you draw?
Check all the references from https://www.nature.com/articles/d41586-020-00885-w for some data, as well as worldometer.
I am trying to find the Japanese government webpage with frequent updates as to the state of patients that were evacuated, still updating the ones that are still in the hospital (! Morbidities...)
So, yes, it is simply misquoting the source that I cited.